app.py

import streamlit as st
import hopsworks
import joblib
import pandas as pd
import numpy as np
import folium
from streamlit_folium import st_folium, folium_static
import json
import time
from datetime import timedelta, datetime
from branca.element import Figure

from functions import decode_features, get_model


def fancy_header(text, font_size=24):
    res = f'<span style="color:#ff5f27; font-size: {font_size}px;">{text}</span>'
    st.markdown(res, unsafe_allow_html=True )


st.title('⛅️Air Quality Prediction Project🌩')

progress_bar = st.sidebar.header('⚙️ Working Progress')
progress_bar = st.sidebar.progress(0)
st.write(36 * "-")
fancy_header('\n📡 Connecting to Hopsworks Feature Store...')

project = hopsworks.login()
fs = project.get_feature_store()
feature_view = fs.get_feature_view(
    name = 'oslo_air_quality_fv',
    version = 1
)

st.write("Successfully connected!✔️")
progress_bar.progress(20)

# st.write(36 * "-")
# fancy_header('\n☁️ Getting batch data from Feature Store...')

# start_date = datetime.now() - timedelta(days=1)
# start_time = int(start_date.timestamp()) * 1000

# # X = feature_view.get_batch_data(start_time=start_time)

# # 1662652800000
# X = feature_view.get_batch_data(start_time=1662652800000)
# progress_bar.progress(50)

# print(X.date.values)

# latest_date_unix = str(X.date.values[0])[:10]
# latest_date = time.ctime(int(latest_date_unix))

# st.write(f"⏱ Data for {latest_date}")

# X = X.drop(columns=["date"]).fillna(0)
# print("X is \n %s" % X)

# data_to_display = decode_features(X, feature_view=feature_view)

# progress_bar.progress(60)

# st.write(36 * "-")
# fancy_header(f"🗺 Processing the map...")

# fig = Figure(width=550,height=350)

# my_map = folium.Map(location=[58, 20], zoom_start=3.71)
# fig.add_child(my_map)
# folium.TileLayer('Stamen Terrain').add_to(my_map)
# folium.TileLayer('Stamen Toner').add_to(my_map)
# folium.TileLayer('Stamen Water Color').add_to(my_map)
# folium.TileLayer('cartodbpositron').add_to(my_map)
# folium.TileLayer('cartodbdark_matter').add_to(my_map)
# folium.LayerControl().add_to(my_map)

# data_to_display = data_to_display[["city", "temp", "humidity",
#                                             "conditions", "aqi"]]

# cities_coords = {("Sundsvall", "Sweden"): [62.390811, 17.306927],
#                  ("Stockholm", "Sweden"): [59.334591, 18.063240],
#                  ("Malmo", "Sweden"): [55.604981, 13.003822],
#                  ("Kyiv", "Ukraine"): [50.450001, 30.523333]}

# # if "Kyiv" in data_to_display["city"]:
# #     cities_coords[("Kyiv", "Ukraine")]: [50.450001, 30.523333]
# #     pass

# data_to_display = data_to_display.set_index("city")

# cols_names_dict = {"temp": "Temperature",
#                    "humidity": "Humidity",
#                    "conditions": "Conditions",
#                    "aqi": "AQI"}

# data_to_display = data_to_display.rename(columns=cols_names_dict)

# cols_ = ["Temperature", "Humidity", "AQI"]
# data_to_display[cols_] = data_to_display[cols_].apply(lambda x: round(x, 1))

# for city, country in cities_coords:
#     text = f"""
#             <h4 style="color:green;">{city}</h4>
#             <h5 style="color":"green">
#                 <table style="text-align: right;">
#                     <tr>
#                         <th>Country:</th>
#                         <td><b>{country}</b></td>
#                     </tr>
#                     """
#     for column in data_to_display.columns:
#         text += f"""
#                     <tr>
#                         <th>{column}:</th>
#                         <td>{data_to_display.loc[city][column]}</td>
#                     </tr>"""
#     text += """</table>
#                     </h5>"""

#     folium.Marker(
#         cities_coords[(city, country)], popup=text, tooltip=f"<strong>{city}</strong>"
#     ).add_to(my_map)


# # call to render Folium map in Streamlit
# folium_static(my_map)
# progress_bar.progress(80)
# st.sidebar.write("-" * 36)


# model = get_model(project=project,
#                   model_name="gradient_boost_model",
#                   evaluation_metric="f1_score",
#                   sort_metrics_by="max")

# preds = model.predict(X)

# cities = [city_tuple[0] for city_tuple in cities_coords.keys()]
# print("cities are %s" % cities)

# next_day_date = datetime.today() + timedelta(days=1)
# next_day = next_day_date.strftime ('%d/%m/%Y')
# print("preds is %s" % preds)
# df = pd.DataFrame(data=preds, index=cities, columns=[f"AQI Predictions for {next_day}"], dtype=int)

# st.sidebar.write(df)
# progress_bar.progress(100)
# st.button("Re-run")
# # hi